348 citations · 1.4k across the 34 of their papers we have counts for
6 papers · 1 filter
UprightNet: Geometry-Aware Camera Orientation Estimation from Single Images
Wenqi Xian, Zhengqi Li, Matthew Fisher +3
We introduce UprightNet, a learning-based approach for estimating 2DoF camera orientation from a single RGB image of an indoor scene. Unlike recent methods that leverage deep learn…
Learning elementary structures for 3D shape generation and matching
Theo Deprelle, Thibault Groueix, Matthew Fisher +3
We propose to represent shapes as the deformation and combination of learnable elementary 3D structures, which are primitives resulting from training over a collection of shape. We…
Unsupervised cycle-consistent deformation for shape matching
Thibault Groueix, Matthew Fisher, Vladimir G. Kim +2
We propose a self-supervised approach to deep surface deformation. Given a pair of shapes, our algorithm directly predicts a parametric transformation from one shape to the other r…
Deep Parametric Shape Predictions using Distance Fields
Dmitriy Smirnov, Matthew Fisher, Vladimir G. Kim +2
Many tasks in graphics and vision demand machinery for converting shapes into consistent representations with sparse sets of parameters; these representations facilitate rendering,…
Photometric Mesh Optimization for Video-Aligned 3D Object Reconstruction
Chen-Hsuan Lin, Oliver Wang, Bryan C. Russell +4
In this paper, we address the problem of 3D object mesh reconstruction from RGB videos. Our approach combines the best of multi-view geometric and data-driven methods for 3D recons…
BAE-NET: Branched Autoencoder for Shape Co-Segmentation
Zhiqin Chen, Kangxue Yin, Matthew Fisher +2
We treat shape co-segmentation as a representation learning problem and introduce BAE-NET, a branched autoencoder network, for the task. The unsupervised BAE-NET is trained with a…